According to “Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer”, SPIE Press, Bellingham, Wash., 2006, breast cancer is the most common type of cancer in women worldwide. Clear evidence shows that early diagnosis and treatment of breast cancer may significantly increase the change of survival for patients. One sign for early detection of breast cancer may be the presence and appearance of microcalcifications. The National Cancer Institute describes microcalcifications as specks of calcium that may be found in an area of rapidly dividing cells. Many microcalcifications clustered together may be a sign of cancer. Other signs may include dense and/or spiculated masses.
Mammography is the process of using low-dose x-rays to examine the human breast. Mammograms may make it possible to detect microcalcifications. Microcalcifications may appear as small, clustered regions of elevated signal intensity against the varying background density of the x-ray mammogram. The radiometric density of microcalcifications along with their small spatial extent, typically less than 1 mm in each dimension, may result in localized regions of high contrast.
Many prior art systems, computer programs, and methods have been disclosed for automatically detecting signals indicative of suspicious microcalcifications in mammograms. Examples can be seen in U.S. Pat. Nos. 5,999,639, 6,167,146, 6,205,236, and 6,389,157, which are fully incorporated herein by reference. Mammograms may be highlighted at key detection spots for further analysis and diagnosis by a radiologist. Evidence indicates that radiologists who use such automated detection schemes may improve their overall detection rates while also being able to focus on the characterization of different cluster types and subsequent treatment options.
One problem that may reduce the accuracy of automated microcalcification detection schemes is the presence of noise in the mammograms. Noise may appear in the image as a result of the type of sensor used to acquire the mammogram, imaging parameters used in acquiring the mammogram (e.g., dose or exposure), and/or the composition of the breast imaged (e.g., fatty versus dense breast tissue). Noise may be particularly problematic for microcalcification region of interest detection because of the small spatial extent of microcalcifications. The measurable signal of a microcalcification may be quite small, and thus, noise may easily obscure such a signal or masquerade as microcalcification.
Some prior art approaches address this problem by normalizing the noise of a mammogram based on the gray-scale content of the image and a priori knowledge of the characteristics of the source used to capture the image. For example, each sensor of a mammographic x-ray imaging system has a different gray-scale dependent noise model (GDNM). Examples of such prior art approaches may be seen in, for example, “Improved correction for signal dependent noise applied to automatic detection of microcalcifications,” Veldkamp, W. Karssemeijer, N., COMPUTATIONAL IMAGING AND VISION, 1998, VOL 13, pages 169-176; “Noise equalization for detection of microcalcification clusters in direct digital mammogram images,” McLoughlin et al., IEEE Trans Med Imaging, 2004 March; 23 (3):313-20; and Published U.S. Patent Application 2008/0187194, “CAD image normalization.”)
The prior art approaches, however, do not solve the problem of the variation in noise level from image to image. Computer systems that process x-ray images must deal with potentially varying noise statistics from x-ray image to x-ray image. This may be particularly problematic for computer systems that must identify microcalcifications in a wide variety of x-ray images having highly variable noise statistics. Changing the processing parameters of an algorithm in response to the amount of noise in each image is one prior art solution to this problem. (See, for example, U.S. Pat. No. 6,542,628 “Method for detection of elements of interest in a digital radiographic image,” assigned to GE Medical Systems, S.A., where a detection threshold value is changed in response to the amount of measured image noise.)